Artificial Intelligence and Machine Learning

What is the main goal of machine learning?

• To learn a new language

• To cook dinner

• To travel the world

• To develop algorithms that can learn from and make predictions or decisions based on data without being explicitly programmed.

What are the three types of machine learning?

• deep learning

• semi-supervised learning

• supervised learning, unsupervised learning, reinforcement learning

• self-learning

Explain the difference between supervised and unsupervised learning.

• Supervised learning is used for classification tasks only, while unsupervised learning is used for regression tasks only.

• In supervised learning, the model is trained on unlabeled data, while in unsupervised learning, the model is trained on labeled data.

• Supervised learning uses neural networks, while unsupervised learning uses decision trees.

• In supervised learning, the model is trained on labeled data, while in unsupervised learning, the model is trained on unlabeled data.

What is overfitting in machine learning?

• Overfitting is when a model perfectly generalizes the training data, leading to optimal performance on new data.

• Overfitting is when a model learns the training data too well, including noise and random fluctuations, leading to poor performance on new, unseen data.

• Overfitting is when a model ignores the training data completely, resulting in random predictions on new data.

• Overfitting is when a model learns the training data too little, resulting in high accuracy on new data.

What is a neural network and how does it work?

• A neural network is a type of computer hardware that mimics the human brain.

• A neural network is a series of algorithms that recognize underlying relationships in data by passing input data through interconnected nodes called neurons.

• A neural network is a simple linear regression model.

• A neural network works by analyzing data in a single pass through the network.

What is the role of algorithms in machine learning?

• Algorithms in machine learning are used to play music

• Algorithms in machine learning are used to cook food

• Algorithms in machine learning are used to process data, learn from it, and make predictions or decisions.

• Algorithms in machine learning are used to build houses

What is reinforcement learning?

• Reinforcement learning is a type of deep learning

• Reinforcement learning is a type of supervised learning

• Reinforcement learning involves only unsupervised learning techniques

• Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties based on its actions.

How does deep learning differ from traditional machine learning?

• Deep learning does not require large datasets for training, unlike traditional machine learning.

• Deep learning cannot handle unstructured data, which traditional machine learning excels at.

• Deep learning uses neural networks with multiple layers to learn complex patterns directly from data, while traditional machine learning relies on manual feature engineering.

• Deep learning uses decision trees for classification, while traditional machine learning uses neural networks.

What are some common machine learning algorithms used in practice?

• Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbors, Neural Networks

• Naive Bayes

• Principal Component Analysis

• Gradient Boosting

How can bias and variance affect the performance of a machine learning model?

• Bias leads to underfitting, variance leads to overfitting.

• Bias leads to overfitting, variance leads to underfitting.

• Bias and variance always lead to optimal model performance.

• Bias and variance have no impact on model performance.